Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning

Abstract

Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.

Bulk and Mosaic Deletions ofEgfrReveal Regionally Defined Gliogenesis in the Developing Mouse Forebrain

“…Brain hemispheres were submerged in DiBenzyl Ether until they became fully transparent. Cleared brain hemispheres were imaged using a custom-built light sheet microscope, the setup of which was outlined in 79,80 . Acquired images were stitched using TeraStitcher 81 and then visualized in Imaris 9.5 (Oxford Instruments) for preparing the movies through each cleared and stained hemisphere.…”

COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains

“…Emx:MADM mouse brains were split into hemispheres, cleared using the iDISCO+ protocol, 5 and imaged by a custom-built LSFM. 37 , 38 , 39 The LSFM was equipped with an autofocus algorithm to correct the shift between the light-sheet illumination beam and the detection focal plane, and therefore our images were crisp and focused throughout acquisition. Before imaging, illumination beams of different wavelengths were aligned parallel to ensure accurate data processing (see later in Results ).…”

“…Cleared mouse brain hemispheres were imaged using a custom-built light-sheet fluorescence microscope, of which the setup was outlined in. 37 , 38 , 39 Each brain hemisphere was imaged by illumination of 561 nm and 640 nm respectively, with a voxel size of 0.65 × 0.65 × 10 μm 3 . During image acquisition, every 1 mm in z direction, autofocus method would be applied to correct for the drift between the light-sheet plane and the detection focal plane.…”

COMBINe: Automated Detection and Classification of Neurons and Astrocytes in Tissue Cleared Mouse Brains

“…Therefore, we designed a workflow that spanned tissue clearing to quantitative analysis (Figure 1C). Emx:MADM mouse brains were split into hemispheres, cleared using the iDISCO+ protocol (Renier et al, 2016) and imaged by a custom-built LSFM (Li et al, 2021(Li et al, , 2022Moatti et al, 2020). The LSFM was equipped with an autofocus algorithm to correct the shift between the light sheet illumination beam and the detection focal plane, and therefore, our images were crisp and focused throughout acquisition.…”

“…Cleared mouse brain hemispheres were imaged using a custom-built light sheet fluorescence microscope, of which the setup was outlined in (Li et al, 2021(Li et al, , 2022Moatti et al, 2020). Each brain hemisphere was imaged by illumination of 561 nm and 640 nm respectively, with a voxel size of 0.65 × 0.65 × 10 μm 3 .…”